{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook regroups the code sample of the video below, which is a part of the [Hugging Face course](https://huggingface.co/course)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M05L1DhFqcw?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title\n",
    "from IPython.display import HTML\n",
    "\n",
    "HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M05L1DhFqcw?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Install the Transformers and Datasets libraries to run this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install datasets transformers[sentencepiece]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_metric\n",
    "\n",
    "bleu = load_metric(\"bleu\")\n",
    "predictions = [[\"I\", \"have\", \"thirty\", \"six\", \"years\"]]\n",
    "references = [\n",
    "    [[\"I\", \"am\", \"thirty\", \"six\", \"years\", \"old\"], [\"I\", \"am\", \"thirty\", \"six\"]]\n",
    "]\n",
    "bleu.compute(predictions=predictions, references=references)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = [[\"I\", \"have\", \"thirty\", \"six\", \"years\"]]\n",
    "references = [\n",
    "    [[\"I\", \"am\", \"thirty\", \"six\", \"years\", \"old\"], [\"I\", \"am\", \"thirty\", \"six\"]]\n",
    "]\n",
    "bleu.compute(predictions=predictions, references=references)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = [[\"I\", \"have\", \"thirty\", \"six\", \"years\"]]\n",
    "references = [\n",
    "    [[\"I\", \"am\", \"thirty\", \"six\", \"years\", \"old\"], [\"I\", \"am\", \"thirty\", \"six\"]]\n",
    "]\n",
    "bleu.compute(predictions=predictions, references=references)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install sacrebleu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sacrebleu = load_metric(\"sacrebleu\")\n",
    "# SacreBLEU operates on raw text, not tokens\n",
    "predictions = [\"I have thirty six years\"]\n",
    "references = [[\"I am thirty six years old\", \"I am thirty six\"]]\n",
    "sacrebleu.compute(predictions=predictions, references=references)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "What is the BLEU metric?",
   "provenance": []
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
